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FCN Salient Object Detection Using Region Cropping

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Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11729))

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Abstract

An important issue in salient object detection is how to improve the result of saliency map for the reason that it is the basis of many subsequent operations in computer vision. In this paper, we propose a region-based salient object detection model using fully convolutional neural network (FCN) with traditional visual saliency method. We introduce the region cropping and jumping operation into FCN network for a more target-oriented feature extraction, which is a low-level cue based processing. It processes the training images into patches of various sizes and makes these patches jump to convolutional layers with corresponding depths as their input data in training. This operation can preserve the main structure of objects while decrease the background redundancy. In the meantime, it also takes into account topological property, which emphasizes the topological integrity of objects. Experimental results on four datasets show that the proposed model performs effectively on salient object detection compared with other ten approaches, including state-of-the-art ones.

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Acknowledgments

This work was supported in part by National Natural Science Foundation of China under grant 61771145 and 61371148.

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Authors and Affiliations

  1. Department of Electronic Engineering, Fudan University, Shanghai, 200433, China

    Yikai Hua & Xiaodong Gu

Authors
  1. Yikai Hua
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  2. Xiaodong Gu
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Correspondence to Xiaodong Gu .

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Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Praha 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Hua, Y., Gu, X. (2019). FCN Salient Object Detection Using Region Cropping. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_29

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  • DOI: https://doi.org/10.1007/978-3-030-30508-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30507-9

  • Online ISBN: 978-3-030-30508-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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